Detailed implementation of various regression analysis models and concepts on real dataset.

Multiple-Linear-Regression-1. Consider only the below columns and prepare a prediction model for predicting Price of Toyota Corolla.

MITx - MicroMasters Program on Statistics and Data Science - Data Analysis: Statistical Modeling and Computation in Applications - First Project

Stock market prediction on 5 italian companies using VAR model, OLS regressions and LSTM recurrent neural networks over data retrieved from Refinitiv Eikon

This project is about statistically analyzing risk factors for heart disease and performing A/B testing, descriptive and inferential statistics to provide health care plans and strategies to better understand the risk factors assocaited with heart disease and give key insights into what factors contribute most heavily and least heavily to the de…

Estimate the impact of OIL and USD towards CPI using least squares method using R

Streamlit app that visualizes data on CO2 emissions and GDP

A collaborative project looking into the likelihood of Covid-19 infection in the United States.

Homework 1 for the INTL 601 Quantitative Research Methods Course, Prof. David Carlson, Koç University.

Our study focused on using the Big Five personality inventory to predict traits from students' smartphone sensor data collected over 2 months under the Horizon Europe project. Through correlation analyses and machine learning with cross-validation, we showed that predictions are reliable and accurate enough for practical use.

For a real estate firm, building a house price prediction model based upon various factors. Problem - Regression | Algorithm used -Linear Regression using OLS

This project is about to use linear regression to examine the relationship between various economic variables and the mortgage rate in the United States.

ML sprint (OLS, KNN, scikit-learn) from Le Wagon Women in Machine Learning event.

In this notebook we would be learning how to check that whether there is intercacion between two dependent variables or not. After that we would consider or add that interaction variable into our regression model and will monitor the changes in the parametrs.

Simple Linear Regression using Ordinary Least Squares

Used libraries and functions as follows:

This repository contains a collection of assignments completed for the System Identification and Parameter Estimation (TIP7044) course at the Federal University of Ceará during my Master's degree.

Here I have checked and removed for heteroskedasticity .

Applying econometric analyses based on a videogame consoles dataset, using statistical software (Stata) and evaluate the results.

This repository contains a Phase 2 Project for the Data Science Flex Program at the Flatiron School. This project uses linear regression, pandas, numpy and exploratory data analysis using matplotlib and seaborn to predict and analyze home prices in the King County data set..